Visual tracking is expected to find a broad range of applications in computer vision, especially visual surveillance in the field of security and analysis/classification and editing of recorded images in the audio-visual field, or man-machine interfacing and human-to-human interfacing, namely, television conferencing, television telephone and the like. Accordingly, much research has been conducted to improve the tracking accuracy and processing efficiency. Among other things, much conducted is a research where a particle filter is applied to the visual tracking. Here, the particle filter attracts attention as a time series analysis method for analyzing a signal added with non-Gaussian noise that a Kalman filter cannot deal with. In particular, the Condensation (Conditional Density Propagation) algorithm is well known in this research (see Non-Patent Document 1 to Non-Patent Document 3, for instance).
The particle filter is a computation technique to approximate the Bayesian filter, and represents the probability distribution of an object by introducing a finite number of particles as candidates to be tracked (tracking candidates). The probability distribution of an object is used for time-series estimation and prediction. The Condensation algorithm estimates a change over time in probability distribution about the shape of an object to be tracked (tracking object), using the particle filter. More specifically, a candidate having the same shape as that of the tracking object is expressed by a particle, and the existence probability distribution on a parameter space is estimated sequentially by the parameter transition using a motion model and observation for calculating the likelihood of the transition results.    [Non-Patent Document 1] Michael Isard and Andrew Blake: Contour tracking by stochastic propagation of conditional density, Proc. European Conf. on Computer Vision, vol. 1, pp. 343-356, Cambridge, UK (1996).    [Non-Patent Document 2] Michael Isard and Andrew Blake: CONDENSATION—conditional density propagation for visual tracking, Int. J. Computer Vision, 29, 1, 5-28 (1998).    [Non-Patent Document 3] Michael Isard and Andrew Blake: ICondensation: Unifying low-level and high-level tracking in a stochastic framework, Proc 5th European Conf. Computer Vision, 1998.